Research Article
BibTex RIS Cite

DETERMINING SITUATION OF PRE-SERVICE TEACHERS’ ARTIFICIAL INTELLIGENT BASED TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE

Year 2025, Issue: 50, 1 - 35, 31.08.2025
https://doi.org/10.14520/adyusbd.1688360

Abstract

This study was conducted on the basis of AI-TPACK, which was established by integrating the use of artificial intelligence into the theory of technological pedagogical content knowledge and includes the ethical issues dimension. Within this scope, three research questions were answered and eight hypotheses related to the third research question were tested. The study, in which single, causal and correlational survey designs were carried out respectively, was attended by 276 pre-service teachers. While answering the first research question, it was observed that all AI-TPACK dimensions consisting of the answers given for all sub-dimensions were at a moderate level. The answer to the second research question, which tested whether the dependent variable AI-TPACK differed significantly according to the independent variables of gender, university and grade level, was that there was no significant difference. The model consisting of hypotheses based on the relationship between each sub-dimension was analyzed by PLS-SEM and a valid and reliable model emerged in which all paths were significant. Considering the research from a holistic perspective, when the pre-service teachers are considered as future teachers, they should receive other trainings that will increase their AI-TPACK level, and the pre-service teachers' AI-TPACK status should be improved through traditional linkages.

References

  • Akbulut, Y. (2010). Sosyal bilimlerde SPSS uygulamaları: Sık kullanılan istatiksel analizler ve açıklamalı SPSS çözümleri. İdeal Kültür Yayıncılık.
  • Akgün, S. ve Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440.
  • Altunova, N. ve Kalender, B. (2022). Öğretmen Adaylarının Kültürel Değerlere Duyarlı Eğitime Hazırbulunuşluk Düzeylerinin Belirlenmesi. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 19(1), 119-135.
  • Ayan, Ş. (2025). Öğretmenlerin Teknolojik Öz Yeterlilikleri. Ulusal ve Uluslararası Sosyoloji ve Ekonomi Dergisi, 5(8), 292-304.
  • Brouwer, N. ve Korthagen, F. (2005). Can teacher education make a difference?. American educational research journal, 42(1), 153-224.
  • Buckingham. (2022). Developing the ethical framework for AI in education. https://www. buckingham.ac.uk/research-the-institute-for-ethical-ai-in-education/.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2013). Bilimsel araştırma yöntemleri (15. Baskı). Ankara: Pegem Akademi.
  • Chen, X., Zou, D., Xie, H., Cheng, G. ve Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  • Choudhury, S., Deb, J. P., Pradhan, P. ve Mishra, A. (2024). Validation of the Teachers AI-TPACK Scale for the Indian Educational Setting. International Journal of Experimental Research and Review, 43, 119-133.
  • Chiu, T. K. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187-6203.
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2. baskı). Lawrence Erlbaum Associates. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.
  • Cun, A. ve Huang, T. (2024). Generative AI and TPACK in Teacher Education: Pre-service Teachers’ Perspectives. Exploring New Horizons: Generative Artificial Intelligence and Teacher Education, 62.
  • Çelik, İ. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.
  • Çelik, İ., Dindar, M., Muukkonen, H. ve Jarvela, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y
  • Daza, V., Gudmundsdottir, G. B. ve Lund, A. (2021). Partnerships as third spaces for professional practice in initial teacher education: A scoping review. Teaching and Teacher education, 102, 103338.
  • Evertson, C. M., Hawley, W. D. ve Zlotnik, M. (1985). Making a difference in educational quality through teacher education. Journal of teacher education, 36(3), 2-12.
  • Faul, F., Erdfelder, E., Lang, A.-G. ve Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5. baskı). SAGE Publications.
  • Fornell, C. ve Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Ghosh, S. ve Sankar, C. S. (2024). Competency-Based Teacher Education in Gender and Inclusive Practices. Social Science Journal for Advanced Research, 4(5), 18-25.
  • Hair, J.F., Hult, G.T.M., Ringle, C.M. ve Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, CA.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., … Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 1–23. https:// doi.org/10.1007/s40593-021-00239-1
  • Hu, L. T. ve Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  • Huck, S.W. (2008). Reading statistics and research (5. baskı). Boston: Pearson.
  • Jones, M. G. (1989). Gender issues in teacher education. Journal of Teacher Education, 40(1), 33-38.
  • Karasar, N. (2003). Sosyal bilimlerde araştırma yöntemleri. Ankara: Seçkin Yayınları.
  • Kim, S. W. (2024). Development of a TPACK Educational Program to Enhance Pre-service Teachers' Teaching Expertise in Artificial Intelligence Convergence Education. International Journal on Advanced Science, Engineering & Information Technology, 14(1).
  • Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S. ve Kim, H. (2021). Analyzing teacher competency with TPACK for K-12 AI education. KI-Künstliche Intelligenz, 35(2), 139-151.
  • Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford Press. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1–10.
  • Koehler, M. ve Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?. Contemporary Issues in Technology and Teacher Education, 9(1), 60-70.
  • Kreitz-Sandberg, S. ve Lahelma, E. (2021). Global Demands–Local Practices: Working towards Inclusion of Gender Equality in Teacher Education in Finland and Sweden. Nordic Journal of Comparative and International Education (NJCIE), 5(1), 50-68.
  • Labaree, D. F. (2008). An uneasy relationship: The history of teacher education in the university. In Handbook of research on teacher education (ss. 290-306). Routledge.
  • Leahy, S. ve Mishra, P. (2023). TPACK and the Cambrian explosion of AI. İçinde Society for Information Technology & Teacher Education International Conference (ss. 2465-2469). Association for the Advancement of Computing in Education (AACE).
  • Lee, S. C. ve Kang, K. (2024). The effect of ‘Integrated Science Logic & Essay’ class using generative AI on technological pedagogical and content knowledge (TPACK) of pre-service science teachers. Journal of Curriculum Evaluation, 27(1), 133-155.
  • Levene, H. (1960). Robust tests for equality of variances. Contributions to Probability and Statistics, 278-292. Mishra, P., Warr, M. ve Islam, R. (2023). TPACK in the age of ChatGPT and Generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235-251.
  • Moorhouse, B. L. (2024). Beginning and first-year language teachers’ readiness for the generative AI age. Computers and Education: Artificial Intelligence, 6, 100201.
  • Ning, Y., Zhang, C., Xu, B., Zhou, Y. ve Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978.
  • Ouchchy, L., Coin, A. ve Dubljević, V. (2020). AI in the headlines: the portrayal of the ethical issues of artificial intelligence in the media. AI & society, 35, 927-936.
  • Popenici, S. A. ve Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8
  • Preacher, K. J. ve Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.
  • Ringle, C. M., Sarstedt, M., Sinkovics, N. ve Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074.
  • Santosh, K. C. ve Wall, C. (2022). AI and ethical issues. In AI, Ethical Issues and Explainability—Applied Biometrics (pp. 1-20). Singapore: Springer Nature Singapore.
  • Sarstedt, M., Ringle, C. M. ve Hair, J. F. (2021). Partial least squares structural equation modeling. İçinde Handbook of market research (ss. 587-632). Cham: Springer International Publishing.
  • Seufert, S., Guggemos, J. ve Sailer, M. (2021). Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552. https://doi.org/10.1016/j.chb.2020.106552
  • Sönmez, V. ve Alacapınar, F.G. (2013). Örneklendirilmiş bilimsel araştırma yöntemleri (2. Baskı). Ankara: Anı Yayıncılık.
  • Stahl, B. C. (2021). Ethical issues of AI. Artificial Intelligence for a better future: An ecosystem perspective on the ethics of AI and emerging digital technologies, 35-53.
  • Sun, J., Ma, H., Zeng, Y., Han, D. ve Jin, Y. (2023). Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach. Education and Information Technologies, 28(2), 1509-1533.
  • Şengür, S. ve Anagün, S. (2021). Sınıf öğretmenlerinin bilişim teknolojileri kullanım düzeyleri ve eğitimde Web 2.0 uygulamaları. Eskişehir Osmangazi Üniversitesi Türk Dünyası Uygulama ve Araştırma Merkezi Eğitim Dergisi, 6(2), 128-150.
  • Tabachnick, B. G. ve Fidell, L. S. (2013). Using multivariate statistics (6. baskı). Pearson.
  • van den Berg, R., Sleegers, P., Geijsel, F. ve Vandenberghe, R. (2000). Implementation of an innovation: Meeting the concerns of teachers. Studies in Educational Evaluation, 26(4), 331-350.
  • Wang, Y., Liu, C. ve Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116–129.
  • Weiner, G. (2000). A critical review of gender and teacher education in Europe. Pedagogy, Culture and Society, 8(2), 233-247.
  • Zittleman, K. ve Sadker, D. (2002). Gender bias in teacher education texts: New (and old) lessons. Journal of Teacher Education, 53(2), 168-180.

ÖĞRETMEN ADAYLARININ YAPAY ZEKÂ TEKNOLOJİLERİ TABANLI TEKNOLOJİK PEDAGOJİK İÇERİK BİLGİSİ DURUMLARININ BELİRLENMESİ

Year 2025, Issue: 50, 1 - 35, 31.08.2025
https://doi.org/10.14520/adyusbd.1688360

Abstract

Bu çalışma teknolojik pedagojik içerik bilgisi kuramına yapay zekâ kullanımın entegre edilmesi ile oluşturulan ve etik değerlendirme boyutunu da içeren YZ-TPAB temelinde gerçekleştirilmiştir. Araştırma kapsamında üç araştırma sorusuna yanıt aranmış ve üçüncü araştırma sorusuna bağlı olan sekiz hipotez test edilmiştir. Tekil, nedensel ve ilişkisel tarama desenlerinin sırasıyla yürütüldüğü çalışmaya iki farklı üniversitede çeşitli sınıf düzeylerinde öğrenim görmekte olan 276 öğretmen adayı katılmıştır. Veriler Çelik (2023) tarafından geliştirilen YZ-TPAB ölçeği ile toplanmıştır. İlk araştırma sorusu bağlamında, tüm alt boyutlara yönelik verilen yanıtlardan oluşan tüm YZ-TPAB boyutlarının, orta düzeyde (30,05). Her bir alt boyutun birbiri ile ilişkisi temelinde kurulan hipotezlerden oluşan model PLS-SEM ile incelenmiş ve tüm yolların anlamlı olduğu, geçerli ve güvenilir bir model ortaya çıkmıştır (αmin=0,902; AVEmin=0,693; |β|≥ 0,10; R2min=0,594; SRMR=0,033; p=0,000). Araştırmaya bütüncül bir bakış açısıyla bakıldığında öğretmen adaylarının geleceğin öğretmenleri olarak ele alındığında, YZ-TPAB düzeylerini yükseltecek başka eğitimler de almaları gerektiği, öğretmen adaylarının YZ-TPAB durumlarının geleneksel bağımsız değişkenlerin etkisinden sıyrıldığı ve bir oranda öğretmenlikte eşitlik sağlanabilecek koşullara yaklaşıldığı ve YZ-TPAB boyutlarının tamamının birbirini değiştirecek etkiye sahip oldukları sonuçlarına ulaşılmıştır.

References

  • Akbulut, Y. (2010). Sosyal bilimlerde SPSS uygulamaları: Sık kullanılan istatiksel analizler ve açıklamalı SPSS çözümleri. İdeal Kültür Yayıncılık.
  • Akgün, S. ve Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440.
  • Altunova, N. ve Kalender, B. (2022). Öğretmen Adaylarının Kültürel Değerlere Duyarlı Eğitime Hazırbulunuşluk Düzeylerinin Belirlenmesi. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 19(1), 119-135.
  • Ayan, Ş. (2025). Öğretmenlerin Teknolojik Öz Yeterlilikleri. Ulusal ve Uluslararası Sosyoloji ve Ekonomi Dergisi, 5(8), 292-304.
  • Brouwer, N. ve Korthagen, F. (2005). Can teacher education make a difference?. American educational research journal, 42(1), 153-224.
  • Buckingham. (2022). Developing the ethical framework for AI in education. https://www. buckingham.ac.uk/research-the-institute-for-ethical-ai-in-education/.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2013). Bilimsel araştırma yöntemleri (15. Baskı). Ankara: Pegem Akademi.
  • Chen, X., Zou, D., Xie, H., Cheng, G. ve Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  • Choudhury, S., Deb, J. P., Pradhan, P. ve Mishra, A. (2024). Validation of the Teachers AI-TPACK Scale for the Indian Educational Setting. International Journal of Experimental Research and Review, 43, 119-133.
  • Chiu, T. K. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187-6203.
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2. baskı). Lawrence Erlbaum Associates. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.
  • Cun, A. ve Huang, T. (2024). Generative AI and TPACK in Teacher Education: Pre-service Teachers’ Perspectives. Exploring New Horizons: Generative Artificial Intelligence and Teacher Education, 62.
  • Çelik, İ. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.
  • Çelik, İ., Dindar, M., Muukkonen, H. ve Jarvela, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y
  • Daza, V., Gudmundsdottir, G. B. ve Lund, A. (2021). Partnerships as third spaces for professional practice in initial teacher education: A scoping review. Teaching and Teacher education, 102, 103338.
  • Evertson, C. M., Hawley, W. D. ve Zlotnik, M. (1985). Making a difference in educational quality through teacher education. Journal of teacher education, 36(3), 2-12.
  • Faul, F., Erdfelder, E., Lang, A.-G. ve Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5. baskı). SAGE Publications.
  • Fornell, C. ve Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Ghosh, S. ve Sankar, C. S. (2024). Competency-Based Teacher Education in Gender and Inclusive Practices. Social Science Journal for Advanced Research, 4(5), 18-25.
  • Hair, J.F., Hult, G.T.M., Ringle, C.M. ve Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, CA.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., … Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 1–23. https:// doi.org/10.1007/s40593-021-00239-1
  • Hu, L. T. ve Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  • Huck, S.W. (2008). Reading statistics and research (5. baskı). Boston: Pearson.
  • Jones, M. G. (1989). Gender issues in teacher education. Journal of Teacher Education, 40(1), 33-38.
  • Karasar, N. (2003). Sosyal bilimlerde araştırma yöntemleri. Ankara: Seçkin Yayınları.
  • Kim, S. W. (2024). Development of a TPACK Educational Program to Enhance Pre-service Teachers' Teaching Expertise in Artificial Intelligence Convergence Education. International Journal on Advanced Science, Engineering & Information Technology, 14(1).
  • Kim, S., Jang, Y., Choi, S., Kim, W., Jung, H., Kim, S. ve Kim, H. (2021). Analyzing teacher competency with TPACK for K-12 AI education. KI-Künstliche Intelligenz, 35(2), 139-151.
  • Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford Press. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1–10.
  • Koehler, M. ve Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?. Contemporary Issues in Technology and Teacher Education, 9(1), 60-70.
  • Kreitz-Sandberg, S. ve Lahelma, E. (2021). Global Demands–Local Practices: Working towards Inclusion of Gender Equality in Teacher Education in Finland and Sweden. Nordic Journal of Comparative and International Education (NJCIE), 5(1), 50-68.
  • Labaree, D. F. (2008). An uneasy relationship: The history of teacher education in the university. In Handbook of research on teacher education (ss. 290-306). Routledge.
  • Leahy, S. ve Mishra, P. (2023). TPACK and the Cambrian explosion of AI. İçinde Society for Information Technology & Teacher Education International Conference (ss. 2465-2469). Association for the Advancement of Computing in Education (AACE).
  • Lee, S. C. ve Kang, K. (2024). The effect of ‘Integrated Science Logic & Essay’ class using generative AI on technological pedagogical and content knowledge (TPACK) of pre-service science teachers. Journal of Curriculum Evaluation, 27(1), 133-155.
  • Levene, H. (1960). Robust tests for equality of variances. Contributions to Probability and Statistics, 278-292. Mishra, P., Warr, M. ve Islam, R. (2023). TPACK in the age of ChatGPT and Generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235-251.
  • Moorhouse, B. L. (2024). Beginning and first-year language teachers’ readiness for the generative AI age. Computers and Education: Artificial Intelligence, 6, 100201.
  • Ning, Y., Zhang, C., Xu, B., Zhou, Y. ve Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978.
  • Ouchchy, L., Coin, A. ve Dubljević, V. (2020). AI in the headlines: the portrayal of the ethical issues of artificial intelligence in the media. AI & society, 35, 927-936.
  • Popenici, S. A. ve Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8
  • Preacher, K. J. ve Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.
  • Ringle, C. M., Sarstedt, M., Sinkovics, N. ve Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074.
  • Santosh, K. C. ve Wall, C. (2022). AI and ethical issues. In AI, Ethical Issues and Explainability—Applied Biometrics (pp. 1-20). Singapore: Springer Nature Singapore.
  • Sarstedt, M., Ringle, C. M. ve Hair, J. F. (2021). Partial least squares structural equation modeling. İçinde Handbook of market research (ss. 587-632). Cham: Springer International Publishing.
  • Seufert, S., Guggemos, J. ve Sailer, M. (2021). Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552. https://doi.org/10.1016/j.chb.2020.106552
  • Sönmez, V. ve Alacapınar, F.G. (2013). Örneklendirilmiş bilimsel araştırma yöntemleri (2. Baskı). Ankara: Anı Yayıncılık.
  • Stahl, B. C. (2021). Ethical issues of AI. Artificial Intelligence for a better future: An ecosystem perspective on the ethics of AI and emerging digital technologies, 35-53.
  • Sun, J., Ma, H., Zeng, Y., Han, D. ve Jin, Y. (2023). Promoting the AI teaching competency of K-12 computer science teachers: A TPACK-based professional development approach. Education and Information Technologies, 28(2), 1509-1533.
  • Şengür, S. ve Anagün, S. (2021). Sınıf öğretmenlerinin bilişim teknolojileri kullanım düzeyleri ve eğitimde Web 2.0 uygulamaları. Eskişehir Osmangazi Üniversitesi Türk Dünyası Uygulama ve Araştırma Merkezi Eğitim Dergisi, 6(2), 128-150.
  • Tabachnick, B. G. ve Fidell, L. S. (2013). Using multivariate statistics (6. baskı). Pearson.
  • van den Berg, R., Sleegers, P., Geijsel, F. ve Vandenberghe, R. (2000). Implementation of an innovation: Meeting the concerns of teachers. Studies in Educational Evaluation, 26(4), 331-350.
  • Wang, Y., Liu, C. ve Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116–129.
  • Weiner, G. (2000). A critical review of gender and teacher education in Europe. Pedagogy, Culture and Society, 8(2), 233-247.
  • Zittleman, K. ve Sadker, D. (2002). Gender bias in teacher education texts: New (and old) lessons. Journal of Teacher Education, 53(2), 168-180.
There are 54 citations in total.

Details

Primary Language Turkish
Subjects Educational Technology and Computing
Journal Section Articles
Authors

Derya Orhan Göksün 0000-0003-0194-0451

Publication Date August 31, 2025
Submission Date May 1, 2025
Acceptance Date August 24, 2025
Published in Issue Year 2025 Issue: 50

Cite

APA Orhan Göksün, D. (2025). ÖĞRETMEN ADAYLARININ YAPAY ZEKÂ TEKNOLOJİLERİ TABANLI TEKNOLOJİK PEDAGOJİK İÇERİK BİLGİSİ DURUMLARININ BELİRLENMESİ. Adıyaman Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(50), 1-35. https://doi.org/10.14520/adyusbd.1688360